The MadNIS reloaded

IF 4.6 2区物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY

SciPost Physics Pub Date : 2024-07-29 DOI:10.21468/scipostphys.17.1.023

Theo Heimel, Nathan Huetsch, Fabio Maltoni, Olivier Mattelaer, Tilman Plehn, Ramon Winterhalder

引用次数: 0

Abstract

In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS further enhance its efficiency and speed. We validate this implementation for realistic partonic processes and find significant gains from using modern machine learning in event generators.

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重装上阵的疯狂国际学校

为了对大型强子对撞机进行精确而快速的理论预测，我们在 MadGraph 事件发生器中实现了 MadNIS 方法。对 MadNIS 的一系列改进进一步提高了其效率和速度。我们在现实的部分子过程中验证了这一实施方案，并发现在事件发生器中使用现代机器学习可以带来显著的收益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

SciPost Physics Physics and Astronomy-Physics and Astronomy (all)

CiteScore

8.20

自引率

12.70%

发文量

315

审稿时长

10 weeks

期刊介绍： SciPost Physics publishes breakthrough research articles in the whole field of Physics, covering Experimental, Theoretical and Computational approaches. Specialties covered by this Journal: - Atomic, Molecular and Optical Physics - Experiment - Atomic, Molecular and Optical Physics - Theory - Biophysics - Condensed Matter Physics - Experiment - Condensed Matter Physics - Theory - Condensed Matter Physics - Computational - Fluid Dynamics - Gravitation, Cosmology and Astroparticle Physics - High-Energy Physics - Experiment - High-Energy Physics - Theory - High-Energy Physics - Phenomenology - Mathematical Physics - Nuclear Physics - Experiment - Nuclear Physics - Theory - Quantum Physics - Statistical and Soft Matter Physics.